Methods and systems for determining instructions for pulling over an autonomous vehicle

ABSTRACT

Methods and systems for determining instructions for pulling over an autonomous vehicle are described. An example method may involve identifying a region of a road ahead of the autonomous vehicle based on lane boundaries of the road, one or more road boundaries indicating an edge of the road, and a size of the autonomous vehicle. The method may also involve determining a braking profile for reducing the speed of the autonomous vehicle based on the region and a speed of the autonomous vehicle. The method may also involve determining, based on the braking profile, a trajectory such that the autonomous vehicle will travel within the region while reducing the speed of the autonomous vehicle. The method may further involve determining instructions for pulling over and stopping the autonomous vehicle in the region in accordance with the determined trajectory and storing the instructions in a memory accessible by a computing device.

BACKGROUND

Autonomous vehicles use various computing systems to aid in transportingpassengers from one location to another. Some autonomous vehicles mayrequire some initial input or continuous input from an operator, such asa pilot, driver, or passenger. Other systems, for example autopilotsystems, may be used only when the system has been engaged, whichpermits the operator to switch from a manual mode (where the operatorexercises a high degree of control over the movement of the vehicle) toan autonomous mode (where the vehicle essentially drives itself) tomodes that lie somewhere in between.

SUMMARY

The present application discloses embodiments that relate to pullingover an autonomous vehicle. In one aspect, the present applicationdescribes a method. The method may comprise identifying a region of aroad ahead of an autonomous vehicle in which to pull over and stop theautonomous vehicle based on lane boundaries of the road, one or moreroad boundaries indicating an edge of the road, and a size of theautonomous vehicle. The method may also comprise based on a speed of theautonomous vehicle and based on the region, determining a brakingprofile for reducing the speed of the autonomous vehicle whiletravelling within the region. The method may also comprise based on thebraking profile, determining a trajectory such that the autonomousvehicle will travel within the region while reducing the speed of theautonomous vehicle. The method may also comprise determining, by acomputing device that is configured to control the autonomous vehicle,instructions for pulling over and stopping the autonomous vehicle in theregion in accordance with the determined trajectory. The method mayfurther comprise storing the instructions in a memory accessible by thecomputing device.

In another aspect, the present application describes a non-transitorycomputer readable medium having stored thereon executable instructionsthat, upon execution by a computing device, cause the computing deviceto perform functions. The functions may comprise identifying a region ofa road ahead of an autonomous vehicle in which to pull over and stop theautonomous vehicle based on lane boundaries of the road, one or moreroad boundaries indicating an edge of the road, and a size of theautonomous vehicle. The functions may also comprise based on a speed ofthe autonomous vehicle and based on the region, determining a brakingprofile for reducing the speed of the autonomous vehicle within theregion, the braking profile comprising a pre-maneuver phase for reducingthe speed of the autonomous vehicle at a first rate, a maneuver phasefor reducing the speed of the autonomous vehicle at a second rate, and apost-maneuver phase for reducing the speed of the autonomous vehicle ata third rate until the autonomous vehicle is stopped in the region. Thefunctions may also comprise based on the braking profile, determining atrajectory such that the autonomous vehicle will travel within theregion while reducing the speed of the autonomous vehicle. The functionsmay also comprise determining instructions for pulling over and stoppingthe autonomous vehicle in the region in accordance with the determinedtrajectory. The functions may further comprise storing the instructionsfor pulling over and stopping the autonomous vehicle.

In still another aspect, the present application describes a system. Thesystem may comprise at least one processor. The system also may comprisea memory having stored thereon instructions that, upon execution by theat least one processor, cause the system to perform functions. Thefunctions may comprise identifying a region of a road ahead of anautonomous vehicle in which to pull over and stop the autonomous vehiclebased on lane boundaries of the road, one or more road boundariesindicating an edge of the road, and a size of the autonomous vehicle.The functions may also comprise based on a speed of the autonomousvehicle and based on the region, determining a braking profile forreducing the speed of the autonomous vehicle while travelling within theregion. The functions may also comprise based on the braking profile,determining a trajectory such that the autonomous vehicle will travelwithin the region while reducing the speed of the autonomous vehicle.The functions may also comprise determining instructions for pullingover and stopping the autonomous vehicle in the region in accordancewith the determined trajectory. The functions may further comprisestoring the instructions in the memory accessible and executable by theat least one processor so as to enable the system to control theautonomous vehicle.

The foregoing summary is illustrative only and is not intended to be inany way limiting. In addition to the illustrative aspects, embodiments,and features described above, further aspects, embodiments, and featureswill become apparent by reference to the figures and the followingdetailed description.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is a simplified block diagram of an example automobile.

FIG. 2 illustrates an example automobile.

FIG. 3 is a flow chart of an example method for pulling over anautonomous vehicle.

FIG. 4A illustrates aspects of the example method.

FIG. 4B illustrates further aspects of the example method.

FIG. 5 illustrates still further aspects of the example method.

FIG. 6 is a schematic diagram illustrating a conceptual partial view ofan example computer program.

DETAILED DESCRIPTION

The following detailed description describes various features andfunctions of the disclosed systems and methods with reference to theaccompanying figures. In the figures, similar symbols identify similarcomponents, unless context dictates otherwise. The illustrative systemand method embodiments described herein are not meant to be limiting. Itmay be readily understood that certain aspects of the disclosed systemsand methods can be arranged and combined in a wide variety of differentconfigurations, all of which are contemplated herein.

An autonomous vehicle operating on a road or path of travel may beconfigured to identify regions of the road or path in which theautonomous vehicle can pull over and stop. Further, the autonomousvehicle may be configured to periodically identify such regions as ittravels so that in an emergency scenario the autonomous vehicle canquickly find a region in which to pull over.

In general, the autonomous vehicle may be looking for a safe space on oralongside the road to pull over. Some roads, such as those on bridges orthose with obstructions in their shoulder lanes, may not be suitable orsafe for pulling over the autonomous vehicle. As such, the autonomousvehicle may ideally look for a continuous shoulder or region of a roadthat is free of obstacles and otherwise suitable for pulling over.

The autonomous vehicle, or a computing device associated with theautonomous vehicle, may monitor a road ahead in order to identify aregion for pulling over the autonomous vehicle. For example, at a givenmoment, the autonomous vehicle may monitor a section of the road that itwill be driving through over the next ten seconds or other period oftime following the given moment. The autonomous vehicle may use avariety of methods to determine whether the section of the road (e.g.,the identified region) is safe or otherwise suitable for pulling overthe autonomous vehicle. If the autonomous vehicle determines that thesection of the road is not safe or not otherwise suitable for pullingover the autonomous vehicle, the autonomous vehicle may continue drivingalong its normal path and monitor a different section of the road thatis further ahead of the previous section.

The autonomous vehicle may be configured to detect objects ahead of theautonomous vehicle on the road in the region, such as moving vehicles,parked vehicles, traffic cones, and other objects, both static anddynamic. The autonomous vehicle may also be configured to detect typesof lane and road boundaries (e.g., road surface markings, curbs,physical barriers) in the region. Further, the autonomous vehicle maydetermine if such objects would prevent the autonomous vehicle frompulling over in the region, or if the autonomous vehicle can adjust itstrajectory in order to pull over while avoiding such objects. Theautonomous vehicle may also utilize map data associated with the road,and the map data may include information about the objects, the laneboundaries, and the road boundaries, among other information.

Once a region is identified and determined to be suitable for pullingover the autonomous vehicle, the autonomous vehicle may determine abraking profile to employ in order to bring the autonomous vehicle'sspeed down to zero within the region. The braking profile may includemultiple phases, and during each phase the autonomous vehicle may reduceits speed at a respective rate while travelling within the region. Inaddition to reducing the speed of the autonomous vehicle, the autonomousvehicle may likely have to move laterally toward a point within theregion that the autonomous vehicle will come to a stop. Since a sharplateral movement while the autonomous vehicle is braking heavily (e.g.,a high rate of deceleration) can lead to control and stability issues,the braking profile may include lower rates of deceleration for phasesduring which the autonomous vehicle is undergoing the majority (orsubstantially all) of the lateral movement required to pull over theautonomous vehicle. The braking profile may take other forms as well.

In an example method for pulling over the autonomous vehicle, theautonomous vehicle may determine an adjusted trajectory based on thebraking profile. The adjusted trajectory may include a route or path forthe autonomous vehicle to travel within the identified region in orderto pull over. Moreover, the adjusted trajectory may be determined suchthat the autonomous vehicle can navigate to avoid static and/or dynamicobjects within the identified region and, in some examples, avoid staticand/or dynamic objects before the autonomous vehicle reaches theidentified region. After determining the adjusted trajectory, theautonomous vehicle may store in memory instructions for pulling over inaccordance with the identified region, the braking profile, and theadjusted trajectory.

The autonomous vehicle may be configured to periodically or continuouslyidentify safe regions for pullover, determine corresponding brakingprofiles and adjusted trajectories, and store respective instructionsfor pulling over in each identified safe region. If the autonomousvehicle does not execute the instructions to pull over, it may continuedriving along its current trajectory. When the autonomous vehicleexecutes the instructions to pull over, it may switch from its currenttrajectory to the adjusted trajectory. The execution of the instructionsmay be automated, such as in an event in which the autonomous vehicledetects an emergency scenario (e.g., sensor failure, empty gas tank,etc.) and responsively pulls itself over. In other events, includingemergency scenarios, a driver or remote operator of the autonomousvehicle can manually provide a command to execute the instructions topull over the autonomous vehicle. In still other events, the driver candisengage automated driving and manually pull the autonomous vehicleover as well.

An example vehicle control system may be implemented in or may take theform of an automobile. Alternatively, a vehicle control system may beimplemented in or take the form of other vehicles, such as cars, trucks,motorcycles, buses, boats, airplanes, helicopters, lawn mowers,recreational vehicles, amusement park vehicles, farm equipment,construction equipment, trams, golf carts, trains, and trolleys. Othervehicles are possible as well.

Further, an example system may take the form of a non-transitorycomputer-readable medium, which has program instructions stored thereonthat are executable by at least one processor to provide thefunctionality described herein. An example system may also take the formof an automobile or a subsystem of an automobile that includes such anon-transitory computer-readable medium having such program instructionsstored thereon.

Referring now to the Figures, FIG. 1 is a simplified block diagram of anexample automobile 100, in accordance with an example embodiment.Components coupled to or included in the automobile 100 may include apropulsion system 102, a sensor system 104, a control system 106,peripherals 108, a power supply 110, a computing device 111, and a userinterface 112. The computing device 111 may include a processor 113, anda memory 114. The computing device 111 may be a controller, or part ofthe controller, of the automobile 100. The memory 114 may includeinstructions 115 executable by the processor 113, and may also store mapdata 116. Components of the automobile 100 may be configured to work inan interconnected fashion with each other and/or with other componentscoupled to respective systems. For example, the power supply 110 mayprovide power to all the components of the automobile 100. The computingdevice 111 may be configured to receive information from and control thepropulsion system 102, the sensor system 104, the control system 106,and the peripherals 108. The computing device 111 may be configured togenerate a display of images on and receive inputs from the userinterface 112.

In other examples, the automobile 100 may include more, fewer, ordifferent systems, and each system may include more, fewer, or differentcomponents. Additionally, the systems and components shown may becombined or divided in any number of ways.

The propulsion system 102 may be configured to provide powered motionfor the automobile 100. As shown, the propulsion system 102 includes anengine/motor 118, an energy source 120, a transmission 122, andwheels/tires 124.

The engine/motor 118 may be or include any combination of an internalcombustion engine, an electric motor, a steam engine, and a Stirlingengine. Other motors and engines are possible as well. In some examples,the propulsion system 102 could include multiple types of engines and/ormotors. For instance, a gas-electric hybrid car could include a gasolineengine and an electric motor. Other examples are possible.

The energy source 120 may be a source of energy that powers theengine/motor 118 in full or in part. That is, the engine/motor 118 maybe configured to convert the energy source 120 into mechanical energy.Examples of energy sources 120 include gasoline, diesel, otherpetroleum-based fuels, propane, other compressed gas-based fuels,ethanol, solar panels, batteries, and other sources of electrical power.The energy source(s) 120 could additionally or alternatively include anycombination of fuel tanks, batteries, capacitors, and/or flywheels. Insome examples, the energy source 120 may provide energy for othersystems of the automobile 100 as well.

The transmission 122 may be configured to transmit mechanical power fromthe engine/motor 118 to the wheels/tires 124. To this end, thetransmission 122 may include a gearbox, clutch, differential, driveshafts, and/or other elements. In examples where the transmission 122includes drive shafts, the drive shafts could include one or more axlesthat are configured to be coupled to the wheels/tires 124.

The wheels/tires 124 of automobile 100 could be configured in variousformats, including a unicycle, bicycle/motorcycle, tricycle, orcar/truck four-wheel format. Other wheel/tire formats are possible aswell, such as those including six or more wheels. The wheels/tires 124of automobile 100 may be configured to rotate differentially withrespect to other wheels/tires 124. In some examples, the wheels/tires124 may include at least one wheel that is fixedly attached to thetransmission 122 and at least one tire coupled to a rim of the wheelthat could make contact with the driving surface. The wheels/tires 124may include any combination of metal and rubber, or combination of othermaterials.

The propulsion system 102 may additionally or alternatively includecomponents other than those shown.

The sensor system 104 may include a number of sensors configured tosense information about an environment in which the automobile 100 islocated. As shown, the sensors of the sensor system include a GlobalPositioning System (GPS) module 126, an inertial measurement unit (IMU)128, a radio detection and ranging (RADAR) unit 130, a laser rangefinderand/or light detection and ranging (LIDAR) unit 132, a camera 134, andactuators 136 configured to modify a position and/or orientation of thesensors. The sensor system 104 may include additional sensors as well,including, for example, sensors that monitor internal systems of theautomobile 100 (e.g., an O₂ monitor, a fuel gauge, an engine oiltemperature, etc.). Other sensors are possible as well.

The GPS module 126 may be any sensor configured to estimate a geographiclocation of the automobile 100. To this end, the GPS module 126 mayinclude a transceiver configured to estimate a position of theautomobile 100 with respect to the Earth, based on satellite-basedpositioning data. In an example, the computing device 111 may beconfigured to use the GPS module 126 in combination with the map data116 to estimate a location of a lane boundary on road on which theautomobile 100 may be travelling on. The GPS module 126 may take otherforms as well.

The IMU 128 may be any combination of sensors configured to senseposition and orientation changes of the automobile 100 based on inertialacceleration. In some examples, the combination of sensors may include,for example, accelerometers and gyroscopes. Other combinations ofsensors are possible as well.

The RADAR unit 130 may be considered as an object detection system thatmay be configured to use radio waves to determine characteristics of theobject such as range, altitude, direction, or speed of the object. TheRADAR unit 130 may be configured to transmit pulses of radio waves ormicrowaves that may bounce off any object in a path of the waves. Theobject may return a part of energy of the waves to a receiver (e.g.,dish or antenna), which may be part of the RADAR unit 130 as well. TheRADAR unit 130 also may be configured to perform digital signalprocessing of received signals (bouncing off the object) and may beconfigured to identify the object.

Other systems similar to RADAR have been used in other parts of theelectromagnetic spectrum. One example is LIDAR (light detection andranging), which may be configured to use visible light from lasersrather than radio waves.

The LIDAR unit 132 may include a sensor configured to sense or detectobjects in an environment in which the automobile 100 is located usinglight. Generally, LIDAR is an optical remote sensing technology that canmeasure distance to, or other properties of, a target by illuminatingthe target with light. As an example, the LIDAR unit 132 may include alaser source and/or laser scanner configured to emit laser pulses and adetector configured to receive reflections of the laser pulses. Forexample, the LIDAR unit 132 may include a laser range finder reflectedby a rotating mirror, and the laser is scanned around a scene beingdigitized, in one or two dimensions, gathering distance measurements atspecified angle intervals. In examples, the LIDAR unit 132 may includecomponents such as light (e.g., laser) source, scanner and optics,photo-detector and receiver electronics, and position and navigationsystem.

In an example, The LIDAR unit 132 may be configured to use ultraviolet(UV), visible, or infrared light to image objects and can be used with awide range of targets, including non-metallic objects. In one example, anarrow laser beam can be used to map physical features of an object withhigh resolution.

In examples, wavelengths in a range from about 10 micrometers (infrared)to about 250 nm (UV) could be used. Typically light is reflected viabackscattering. Different types of scattering are used for differentLIDAR applications, such as Rayleigh scattering, Mie scattering andRaman scattering, as well as fluorescence. Based on different kinds ofbackscattering, LIDAR can be accordingly called Rayleigh LIDAR, MieLIDAR, Raman LIDAR and Na/Fe/K Fluorescence LIDAR, as examples. Suitablecombinations of wavelengths can allow for remote mapping of objects bylooking for wavelength-dependent changes in intensity of reflectedsignals, for example.

Three-dimensional (3D) imaging can be achieved using both scanning andnon-scanning LIDAR systems. “3D gated viewing laser radar” is an exampleof a non-scanning laser ranging system that applies a pulsed laser and afast gated camera. Imaging LIDAR can also be performed using an array ofhigh speed detectors and a modulation sensitive detectors arraytypically built on single chips using CMOS (complementarymetal-oxide-semiconductor) and hybrid CMOS/CCD (charge-coupled device)fabrication techniques. In these devices, each pixel may be processedlocally by demodulation or gating at high speed such that the array canbe processed to represent an image from a camera. Using this technique,many thousands of pixels may be acquired simultaneously to create a 3Dpoint cloud representing an object or scene being detected by the LIDARunit 132.

A point cloud may include a set of vertices in a 3D coordinate system.These vertices may be defined by X, Y, and Z coordinates, for example,and may represent an external surface of an object. The LIDAR unit 132may be configured to create the point cloud by measuring a large numberof points on the surface of the object, and may output the point cloudas a data file. As the result of a 3D scanning process of the object bythe LIDAR unit 132, the point cloud can be used to identify andvisualize the object.

In one example, the point cloud can be directly rendered to visualizethe object. In another example, the point cloud may be converted topolygon or triangle mesh models through a process that may be referredto as surface reconstruction. Example techniques for converting a pointcloud to a 3D surface may include Delaunay triangulation, alpha shapes,and ball pivoting. These techniques include building a network oftriangles over existing vertices of the point cloud. Other exampletechniques may include converting the point cloud into a volumetricdistance field and reconstructing an implicit surface so defined througha marching cubes algorithm.

The camera 134 may be any camera (e.g., a still camera, a video camera,etc.) configured to capture images of the environment in which theautomobile 100 is located. To this end, the camera may be configured todetect visible light, or may be configured to detect light from otherportions of the spectrum, such as infrared or ultraviolet light. Othertypes of cameras are possible as well. The camera 134 may be atwo-dimensional detector, or may have a three-dimensional spatial range.In some examples, the camera 134 may be, for example, a range detectorconfigured to generate a two-dimensional image indicating a distancefrom the camera 134 to a number of points in the environment. To thisend, the camera 134 may use one or more range detecting techniques. Forexample, the camera 134 may be configured to use a structured lighttechnique in which the automobile 100 illuminates an object in theenvironment with a predetermined light pattern, such as a grid orcheckerboard pattern and uses the camera 134 to detect a reflection ofthe predetermined light pattern off the object. Based on distortions inthe reflected light pattern, the automobile 100 may be configured todetermine the distance to the points on the object. The predeterminedlight pattern may comprise infrared light, or light of anotherwavelength.

The actuators 136 may, for example, be configured to modify a positionand/or orientation of the sensors.

The sensor system 104 may additionally or alternatively includecomponents other than those shown.

The control system 106 may be configured to control operation of theautomobile 100 and its components. To this end, the control system 106may include a steering unit 138, a throttle 140, a brake unit 142, asensor fusion algorithm 144, a computer vision system 146, a navigationor pathing system 148, and an obstacle avoidance system 150.

The steering unit 138 may be any combination of mechanisms configured toadjust the heading or direction of the automobile 100.

The throttle 140 may be any combination of mechanisms configured tocontrol the operating speed and acceleration of the engine/motor 118and, in turn, the speed and acceleration of the automobile 100.

The brake unit 142 may be any combination of mechanisms configured todecelerate the automobile 100. For example, the brake unit 142 may usefriction to slow the wheels/tires 124. As another example, the brakeunit 142 may be configured to be regenerative and convert the kineticenergy of the wheels/tires 124 to electric current. The brake unit 142may take other forms as well.

The sensor fusion algorithm 144 may include an algorithm (or a computerprogram product storing an algorithm) executable by the computing device111, for example. The sensor fusion algorithm 144 may be configured toaccept data from the sensor system 104 as an input. The data mayinclude, for example, data representing information sensed at thesensors of the sensor system 104. The sensor fusion algorithm 144 mayinclude, for example, a Kalman filter, a Bayesian network, or anotheralgorithm. The sensor fusion algorithm 144 further may be configured toprovide various assessments based on the data from the sensor system104, including, for example, evaluations of individual objects and/orfeatures in the environment in which the automobile 100 is located,evaluations of particular situations, and/or evaluations of possibleimpacts based on particular situations. Other assessments are possibleas well

The computer vision system 146 may be any system configured to processand analyze images captured by the camera 134 in order to identifyobjects and/or features in the environment in which the automobile 100is located, including, for example, lane information, traffic signalsand obstacles. To this end, the computer vision system 146 may use anobject recognition algorithm, a Structure from Motion (SFM) algorithm,video tracking, or other computer vision techniques. In some examples,the computer vision system 146 may additionally be configured to map theenvironment, track objects, estimate speed of objects, etc.

The navigation and pathing system 148 may be any system configured todetermine a driving path for the automobile 100. The navigation andpathing system 148 may additionally be configured to update the drivingpath dynamically while the automobile 100 is in operation. In someexamples, the navigation and pathing system 148 may be configured toincorporate data from the sensor fusion algorithm 144, the GPS module126, and one or more predetermined maps so as to determine the drivingpath for the automobile 100.

The obstacle avoidance system 150 may be any system configured toidentify, evaluate, and avoid or otherwise negotiate obstacles in theenvironment in which the automobile 100 is located.

The control system 106 may additionally or alternatively includecomponents other than those shown.

Peripherals 108 may be configured to allow the automobile 100 tointeract with external sensors, other automobiles, and/or a user. Tothis end, the peripherals 108 may include, for example, a wirelesscommunication system 152, a touchscreen 154, a microphone 156, and/or aspeaker 158.

The wireless communication system 152 may be any system configured to bewirelessly coupled to one or more other automobiles, sensors, or otherentities, either directly or via a communication network. To this end,the wireless communication system 152 may include an antenna and achipset for communicating with the other automobiles, sensors, or otherentities either directly or over an air interface. The chipset orwireless communication system 152 in general may be arranged tocommunicate according to one or more other types of wirelesscommunication (e.g., protocols) such as Bluetooth, communicationprotocols described in IEEE 802.11 (including any IEEE 802.11revisions), cellular technology (such as GSM, CDMA, UMTS, EV-DO, WiMAX,or LTE), Zigbee, dedicated short range communications (DSRC), and radiofrequency identification (RFID) communications, among otherpossibilities. The wireless communication system 152 may take otherforms as well.

The touchscreen 154 may be used by a user to input commands to theautomobile 100. To this end, the touchscreen 154 may be configured tosense at least one of a position and a movement of a user's finger viacapacitive sensing, resistance sensing, or a surface acoustic waveprocess, among other possibilities. The touchscreen 154 may be capableof sensing finger movement in a direction parallel or planar to thetouchscreen surface, in a direction normal to the touchscreen surface,or both, and may also be capable of sensing a level of pressure appliedto the touchscreen surface. The touchscreen 154 may be formed of one ormore translucent or transparent insulating layers and one or moretranslucent or transparent conducting layers. The touchscreen 154 maytake other forms as well.

The microphone 156 may be configured to receive audio (e.g., a voicecommand or other audio input) from a user of the automobile 100.Similarly, the speakers 158 may be configured to output audio to theuser of the automobile 100.

The peripherals 108 may additionally or alternatively include componentsother than those shown.

The power supply 110 may be configured to provide power to some or allof the components of the automobile 100. To this end, the power supply110 may include, for example, a rechargeable lithium-ion or lead-acidbattery. In some examples, one or more banks of batteries could beconfigured to provide electrical power. Other power supply materials andconfigurations are possible as well. In some examples, the power supply110 and energy source 120 may be implemented together, as in someall-electric cars.

The processor 113 included in the computing device 111 may comprise oneor more general-purpose processors and/or one or more special-purposeprocessors (e.g., image processor, digital signal processor, etc.). Tothe extent that the processor 113 includes more than one processor, suchprocessors could work separately or in combination. The computing device111 may be configured to control functions of the automobile 100 basedon input received through the user interface 112, for example.

The memory 114, in turn, may comprise one or more volatile and/or one ormore non-volatile storage components, such as optical, magnetic, and/ororganic storage, and the memory 114 may be integrated in whole or inpart with the processor 113. The memory 114 may contain the instructions115 (e.g., program logic) executable by the processor 113 to executevarious automobile functions, including any of the functions or methodsdescribed herein.

The components of the automobile 100 could be configured to work in aninterconnected fashion with other components within and/or outside theirrespective systems. To this end, the components and systems of theautomobile 100 may be communicatively linked together by a system bus,network, and/or other connection mechanism (not shown).

Further, while each of the components and systems is shown to beintegrated in the automobile 100, in some examples, one or morecomponents or systems may be removably mounted on or otherwise connected(mechanically or electrically) to the automobile 100 using wired orwireless connections.

The automobile 100 may include one or more elements in addition to orinstead of those shown. For example, the automobile 100 may include oneor more additional interfaces and/or power supplies. Other additionalcomponents are possible as well. In these examples, the memory 114 mayfurther include instructions executable by the processor 113 to controland/or communicate with the additional components.

FIG. 2 illustrates an example automobile 200, in accordance with anembodiment. In particular, FIG. 2 shows a Right Side View, Front View,Back View, and Top View of the automobile 200. Although automobile 200is illustrated in FIG. 2 as a car, other examples are possible. Forinstance, the automobile 200 could represent a truck, a van, asemi-trailer truck, a motorcycle, a golf cart, an off-road vehicle, or afarm vehicle, among other examples. As shown, the automobile 200includes a first sensor unit 202, a second sensor unit 204, a thirdsensor unit 206, a wireless communication system 208, and a camera 210.

Each of the first, second, and third sensor units 202-206 may includeany combination of global positioning system sensors, inertialmeasurement units, RADAR units, LIDAR units, cameras, lane detectionsensors, and acoustic sensors. Other types of sensors are possible aswell.

While the first, second, and third sensor units 202 are shown to bemounted in particular locations on the automobile 200, in some examplesthe sensor unit 202 may be mounted elsewhere on the automobile 200,either inside or outside the automobile 200. Further, while only threesensor units are shown, in some examples more or fewer sensor units maybe included in the automobile 200.

In some examples, one or more of the first, second, and third sensorunits 202-206 may include one or more movable mounts on which thesensors may be movably mounted. The movable mount may include, forexample, a rotating platform. Sensors mounted on the rotating platformcould be rotated so that the sensors may obtain information from eachdirection around the automobile 200. Alternatively or additionally, themovable mount may include a tilting platform. Sensors mounted on thetilting platform could be tilted within a particular range of anglesand/or azimuths so that the sensors may obtain information from avariety of angles. The movable mount may take other forms as well.

Further, in some examples, one or more of the first, second, and thirdsensor units 202-206 may include one or more actuators configured toadjust the position and/or orientation of sensors in the sensor unit bymoving the sensors and/or movable mounts. Example actuators includemotors, pneumatic actuators, hydraulic pistons, relays, solenoids, andpiezoelectric actuators. Other actuators are possible as well.

The wireless communication system 208 may be any system configured towirelessly couple to one or more other automobiles, sensors, or otherentities, either directly or via a communication network as describedabove with respect to the wireless communication system 152 in FIG. 1.While the wireless communication system 208 is shown to be positioned ona roof of the automobile 200, in other examples the wirelesscommunication system 208 could be located, fully or in part, elsewhere.

The camera 210 may be any camera (e.g., a still camera, a video camera,etc.) configured to capture images of the environment in which theautomobile 200 is located. To this end, the camera 210 may take any ofthe forms described above with respect to the camera 134 in FIG. 1.While the camera 210 is shown to be mounted inside a front windshield ofthe automobile 200, in other examples the camera 210 may be mountedelsewhere on the automobile 200, either inside or outside the automobile200.

The automobile 200 may include one or more other components in additionto or instead of those shown.

A control system of the automobile 200 may be configured to control theautomobile 200 in accordance with a control strategy from among multiplepossible control strategies. The control system may be configured toreceive information from sensors coupled to the automobile 200 (on oroff the automobile 200), modify the control strategy (and an associateddriving behavior) based on the information, and control the automobile200 in accordance with the modified control strategy. The control systemfurther may be configured to monitor the information received from thesensors, and continuously evaluate driving conditions; and also may beconfigured to modify the control strategy and driving behavior based onchanges in the driving conditions.

FIG. 3 is a flow chart of an example method 300 for adjusting a speed ofa vehicle. The method 300 may include one or more operations, functions,or actions as illustrated by one or more of blocks 302-310. Although theblocks are illustrated in a sequential order, these blocks may in someinstances be performed in parallel, and/or in a different order thanthose described herein. Also, the various blocks may be combined intofewer blocks, divided into additional blocks, and/or removed based uponthe desired implementation.

In addition, for the method 300 and other processes and methodsdisclosed herein, the flowchart shows functionality and operation of onepossible implementation of present embodiments. In this regard, eachblock may represent a module, a segment, or a portion of program code,which includes one or more instructions executable by a processor forimplementing specific logical functions or steps in the process. Theprogram code may be stored on any type of computer readable medium ormemory, for example, such as a storage device including a disk or harddrive. The computer readable medium may include a non-transitorycomputer readable medium, for example, such as computer-readable mediathat stores data for short periods of time like register memory,processor cache and Random Access Memory (RAM). The computer readablemedium may also include non-transitory media or memory, such assecondary or persistent long term storage, like read only memory (ROM),optical or magnetic disks, compact-disc read only memory (CD-ROM), forexample. The computer readable media may also be any other volatile ornon-volatile storage systems. The computer readable medium may beconsidered a computer readable storage medium, a tangible storagedevice, or other article of manufacture, for example.

In addition, for the method 300 and other processes and methodsdisclosed herein, each block in FIG. 3 may represent circuitry that iswired to perform the specific logical functions in the process. For thesake of example, the method 300 shown in FIG. 3 will be described asimplemented by an example computing device, such as the computing device111 in FIG. 1. The method 300 can also be described as implemented by anautonomous vehicle, as the computing device may be onboard the vehicleor may be off-board but in wireless communication with the vehicle.Therefore the terms “computing device” and “autonomous vehicle” can beinterchangeable herein. However, in some examples, the computing devicemay be configured to control the vehicle in an autonomous orsemi-autonomous operation mode. It should be understood that otherentities or combinations of entities can implement one or more steps ofthe example method 300.

At block 302, the method 300 includes identifying a region of a roadahead of an autonomous vehicle in which to pull over and stop theautonomous vehicle based on lane boundaries of the road, one or moreroad boundaries indicating an edge of the road, and a size of theautonomous vehicle. As the autonomous vehicle travels, the computingdevice may be configured to periodically identify regions of interest(e.g., every 100 milliseconds). The regions of interest may includemultiple regions ahead of the autonomous vehicle and to the left and/orright of the autonomous vehicle. In some embodiments, the computingdevice may gather a list of regions and then analyze the regions todetermine a region that is most safe or otherwise preferable for pullingover the autonomous vehicle. For example, the computing device may pullover the autonomous vehicle in the closest safe region rather than afarther region, depending on the urgency of the pullover. Other examplesare also possible, and a variety of factors may determine which regionout of multiple safe regions to pull over in, aside from the urgency ofthe pullover.

Further, a given region may be identified based on its distance from theautonomous vehicle and/or based on how soon the autonomous vehicle willenter the given region as the autonomous vehicle continues along itscurrent trajectory. For example, at a given point in time and at a givenlocation on the road, a region of interest may be a section of the roadthat the autonomous vehicle will be driving over during the next tenseconds following the given point in time. Other examples are alsopossible.

In some embodiments, the computing device identifying the region maycomprise the computing device referring to a predetermined map stored inmemory either locally at the computing device or remotely at anothercomputing device (and/or the computing device dynamically building a mapof the road ahead of the autonomous vehicle). The roads, paths, andother drivable areas of the dynamic map and/or the predetermined map mayinclude labels for identified lanes, lane boundaries, road boundaries,and connections between lanes (e.g., which lanes can feed into otheradjacent lanes). Such labels may include information indicating types oflanes of the road (e.g., traffic lane, passing lane, emergency lane,turning lane, bus lane, etc.), types of lane boundaries (e.g., whitelines, yellow lines, other road surface markings and/or mechanicalmarkings, etc.), types of road boundaries (e.g., regular curbs, redcurbs, sidewalks, guard rails, other barriers, etc.) road intersections,one or more obstacles ahead of the autonomous vehicle, and otherinformation about the road or areas adjacent to the road.

In some embodiments, the respective identified regions ahead of theautonomous vehicle may have an associated priority and/or quality score.For example, a quality and/or priority determined by the computingdevice may indicate to the computing device that a suitable region forpullover that is farther ahead of the autonomous vehicle is moresuitable for pullover than another suitable region that is closer to theautonomous vehicle. Further, the pullover region that is farther awaymay have more pullover space than the closer region, and there may notbe an immediate need to pullover (e.g., no emergency scenario).Alternatively, the computing device may pull over the autonomous vehiclein the suitable closer region with the lower quality score and/orpriority if an emergency scenario is detected. Other examples are alsopossible.

In some scenarios, the computing device may be configured to filter outand disregard information from the map data. In such scenarios, thecomputing device may not consider certain regions as safe for pullingover the autonomous vehicle. For example, regions where a lane'sboundary is marked as red or blue, or the lane is labeled as a “noparking” lane, may be ignored by the computing device. Other examplesare also possible. However, it should be understood that in certainemergency scenarios, the computing device may be configured to pull overthe autonomous vehicle in ignored regions if a pullover is necessary orrequired. Further, in general, the computing device may be configured topull over the autonomous vehicle in regions that have been identified,but deemed unsafe or unsatisfactory for pullover by the computingdevice, such as regions that may have space for the autonomous vehicleto pullover and park, but are labeled as “no parking zones.”

In some embodiments, the computing device identifying the region maycomprise the computing device utilizing the predetermined map data.Namely, the computing device may take identified regions of interestfrom the predetermined map and divide the regions into smaller “chunks”of similar or varying length, while the width of the chunks may be thesame as the width of the road (e.g., from a leftmost road boundary to arightmost road boundary) or may be a longer width. For example, eachregion may be divided into chunks of 50 centimeters in length. Asanother example, each region may be divided into chunks of 1 meter inlength. In other examples, each region may be small enough in size wherethey do not need to be divided into chunks. Other examples are alsopossible.

The computing device may then apply one or more methods/algorithms toeach chunk to determine if the given chunk and/or the respective regionthat the given chunk is in are safe or otherwise ideal for pullover. Oneexample method/algorithm may include the computing device firstidentifying the rightmost lane (L_r). The computing device may thenobtain, from the predetermined map data, a set of road and laneboundaries (and possibly other boundaries), and identify the boundariesto the right of L_r. The computing device may then search through theboundaries in order to identify the rightmost boundary (B_l) which canbe considered the edge of L_r. For example, if there is a bike lane tothe right of L_r, the computing device may be configured to identify theleft edge of the bike lane as B_l. The computing device may also searchthrough the boundaries in order to identify the rightmost boundary whichcan be considered the edge of the road (B_e). After identifying B_l andB_e, the computing device may then determine the lateral distance fromthe center of L_r to B_l (D_l), and the lateral distance from the centerof L_r to B_e (D_e). Further, if D_l plus the width of the autonomousvehicle (w) plus a threshold distance (T_p) is less than D_e, thecomputing device may determine that the given chunk is safe or otherwisesuitable to pull over the autonomous vehicle at that given chunk of therespective region. In other words, this result indicates that the edgeof the road is further from the rightmost lane than the width of the carplus a buffer distance (T_p). Alternatively, if D_l plus w plus T_p isgreater than D_e, the computing device may determine that the givenchunk is not safe or otherwise not suitable to pull over the autonomousvehicle at that given chunk of the respective region.

In some embodiments, the threshold distance, T_p may be a constant valueused in many or all pullover scenarios. Alternatively, in otherembodiments, T_p may vary per scenario based on one or more factorsincluding the predetermined map data, types of boundaries on the road,and other factors, some being associated with autonomous vehicle'ssurrounding environment. For example, a width of the road may determinea value for T_p. When streets are narrow, T_p may be larger so that whenthe autonomous vehicle pulls over and parks, it may not block passingtraffic. In general, a positive value for T_p may require a wider regionfor pulling over the autonomous vehicle, which may also allow for lessprecise predetermined map data. As another example, a speed limit of theroad may determine a value for T_p. In low speed areas, such as onresidential streets, T_p may have a negative value, which would allowthe autonomous vehicle to pull over while leaving a portion of thevehicle in the road. Further on residential streets, for instance, theautonomous vehicle may be able to pull over safely while still leavingroom for passing traffic. As yet another example, road surface markingsmay determine a value for T_p. When curbs are marked as loading zones,for instance, T_p may have a negative value (e.g., a negative value asmuch as the width of the autonomous vehicle, −w). As still anotherexample, T_p may have a value of zero for some roads, in which case thegiven chunk may be considered viable for pullover if, after pulling overthe autonomous vehicle to the side of the road, no portion of the carwould be left in the road. Many other factors are possible as well fordetermining a value of T_p.

In some embodiments, different known regions of a road indicated bypredetermined map data may each have a respective predetermined T_pincluded in memory and associated with the predetermined map data.Additionally or alternatively, in other embodiments, the computingdevice may determine T_p for a particular region after identifying theparticular region using live sensor data.

In some embodiments, the computing device may determine T_p by use of adecision tree or decision forest. Moreover, T_p may have an initialvalue, and the computing device may run though a decision tree in orderto add or subtract from the initial value. For example, the initialvalue of T_p may be zero, and the computing device may first check aregion of the road for its speed limit. If the speed limit exceeds acertain threshold (e.g., 45 mph), a value may be added to T_p (e.g.,+1.0 meters). If the speed limit is below a certain threshold (e.g., 15mph), a value may be subtracted from T_p (e.g., −0.5 meters). Next, thecomputing device may determine if D_l is greater or less than one ormore thresholds. Third, the computing device may then check if theregion includes a loading zone or other special type of zone. Thecomputing device may then determine other factors/values as well, andadjust T_p accordingly. It should be understood that other examples ofdecision trees/forests are possible as well and not limited to theaforementioned example.

In some embodiments, the computing device may also be configured to pullover the autonomous vehicle to the left of the road rather than theright. In such embodiments, an algorithm may be utilized that is similarto the algorithm described above. For example, instead of searching forand identifying rightmost boundaries, the computing device may beconfigured to search for and identify leftmost boundaries. Additionally,the computing device may be configured to search for any road marking,physical barrier, etc. that separates the leftmost lane from oncomingtraffic, instead of searching for and identifying an edge of the road.Other examples are also possible.

In some embodiments, the computing device may be configured to takechunks from one region that have been identified as safe chunks andcombine them with safe chunks from another region. In other embodiments,instead of the computing device first identifying regions of interestand then dividing them into chunks, the computing device may beconfigured to identify smaller chunks (e.g., smaller regions)individually as it drives. In still other embodiments, chunks thatinclude portions of a bridge or other road structure may not beconsidered viable for pullover if there is no place to park withoutblocking traffic.

In some embodiments, the computing device identifying the region may bebased on an identification of one or more objects ahead of theautonomous vehicle on the road. Additionally or alternatively toidentifying objects ahead of (or substantially in front of) theautonomous vehicle (e.g., in substantially the same lane as theautonomous vehicle or an adjacent lane), the computing device can beconfigured to identify other objects within an environment of theautonomous vehicle, including objects to the left and right of theautonomous vehicle (e.g., adjacent lanes on a road), and/or behind theautonomous vehicle, for example (e.g., such as another vehicletravelling behind the autonomous vehicle that may be ready to pass theautonomous vehicle and perhaps enter the identified region(s)). Thecomputing device may also be configured to identify objects near and/oradjacent to the road as well, such as buildings, fire hydrants, or otherobjects at or beyond the road boundaries. The objects may be within alongitudinal distance threshold from the autonomous vehicle and/orwithin a lateral distance threshold from the autonomous vehicle.

The one or more objects may include pedestrians, traffic control objects(e.g., stop signs, traffic cones, traffic lights), constructionequipment, buildings, vehicles (e.g., cars, trucks, bicycles), and otherobjects. In addition to identifying the objects, the computing devicemay be configured to determine respective characteristics of eachobject. For example, the computing device may be configured to determinea type of an object or classify the object (e.g., car or truck, car ormotorcycle, traffic sign or a pedestrian, etc.). Further, the computingdevice can determine whether the object is moving or stationary (e.g., aparked car).

The computing device may be configured to estimate a size (e.g., widthand length) and weight of the object. Further, the computing device maybe configured to determine a direction of motion of the object, such asif the object is moving towards the autonomous vehicle or away from thevehicle. Still further, the computing device may be configured todetermine a transmission type (e.g., manual and automatic) andtransmission mode of the object, such as whether the object is in park,drive, reverse, or neutral transmission mode. Yet still further, thecomputing device may be configured to determine a position of the objectin a respective lane on the road or path of travel, and how close theobject may be to lane boundaries. In some examples, the computing devicemay be configured to determine relative longitudinal speed and lateralspeed of the object with respect to the autonomous vehicle. Thesecharacteristics are examples, and other characteristics can bedetermined as well.

To identify the objects and characteristics of the objects, as well asother information associated with the autonomous vehicle's environment,the computing device may be configured to use the sensors and devicescoupled to the autonomous vehicle, which may include sensors and modulesused in the navigation and pathing system 148 in FIG. 1, as describedabove. Object data and other environmental data obtained from theautonomous vehicle's sensors may include information associated withlive lane and road boundaries. The live boundaries may be similar tothose encoded in the predetermined map, but the object data from thesensor(s) may take into account any changes in the environment betweenthe construction of the predetermined map and the present time.

In some embodiments, after obtaining sensor data for a given regionahead of the autonomous vehicle, the computing device may be configuredto run one or more algorithms over the given region in order todetermine whether they are safe for pullover. The one or more algorithmsmay be the same as the methods/algorithms described above with respectto the chunks of the given region. Alternatively, the one or morealgorithms may be a modified version of the methods/algorithms describedabove, and information regarding the given region as obtained by thesensor data may take precedence over information regarding the givenregion from the predetermined map data. For example, more conservativeboundaries may be utilized with a modified algorithm (e.g., live sensordata may detect a road boundary to be closer to the autonomous vehiclethan the predetermined map indicated the road boundary to be). Further,certain identified objects in the given region may effectively act asadditional road edge boundaries. For example, if an object to the rightof the autonomous vehicle is closer to the center of the autonomousvehicle's lane than a rightmost road boundary, the left edge of theobject may serve as the rightmost road boundary instead the rightmostedge of the road. Other examples are also possible.

Sensor data may include data obtained, for example, from a camera, suchas the camera 134 in FIG. 1 or the camera 210 in FIG. 2 or any otherimage-capture device, may be coupled to the autonomous vehicle and maybe in communication with the computing device. The camera may beconfigured to capture images or a video of the path/road of travel andvicinity of the path/road of travel. The computing device may beconfigured to receive the images or video and identify, using imageprocessing techniques for example, objects depicted in the image or thevideo. The computing device may be configured compare portions of theimages to templates of objects to identify the objects, for example.

In another example, the computing device may be configured to receive,from a LIDAR device (e.g., the LIDAR unit 132 in FIG. 1) coupled to theautonomous vehicle and in communication with the computing device,LIDAR-based information that may include a three-dimensional (3D) pointcloud. The 3D point cloud may include points corresponding to lightemitted from the LIDAR device and reflected from objects on the road orin the vicinity of the road.

As described with respect to the LIDAR unit 132 in FIG. 1, operation ofthe LIDAR device may involve an optical remote sensing technology thatenables measuring properties of scattered light to find range and/orother information of a distant target. The LIDAR device, for example,may be configured to emit laser pulses as a beam, and scan the beam togenerate two dimensional or three dimensional range matrices. In anexample, the range matrices may be used to determine distance to anobject or surface by measuring time delay between transmission of apulse and detection of a respective reflected signal.

In examples, the LIDAR device may be configured to scan an environmentsurrounding the autonomous vehicle in three dimensions. In someexamples, more than one LIDAR device may be coupled to the vehicle toscan a complete 360° horizon of the vehicle. The LIDAR device may beconfigured to provide to the computing device a cloud of point datarepresenting obstacles or objects, which have been hit by the laser, onthe road and the vicinity of the road. The points may be represented bythe LIDAR device in terms of azimuth and elevation angles, in additionto range, which can be converted to (X, Y, Z) point data relative to alocal coordinate frame attached to the autonomous vehicle. Additionally,the LIDAR device may be configured to provide to the computing deviceintensity values of the light or laser reflected off the obstacles thatmay be indicative of a surface type of a given object. Based on suchinformation, the computing device may be configured to identify theobjects and characteristics of the objects such as type of the object,size, speed, whether the object is a traffic sign with a retroreflectivesurface, etc.

In still another example, the computing device may be configured toreceive, from a RADAR device (e.g., the RADAR unit 130 in FIG. 1)coupled to the autonomous vehicle and in communication with thecomputing device, RADAR-based information relating to location andcharacteristics of the objects. The RADAR device may be configured toemit radio waves and receive back the emitted radio waves that bouncedoff the surface of objects on the road and in the vicinity of the road.The received signals or RADAR-based information may be indicative, forexample, of dimensional characteristics of a given object, and mayindicate whether the given object is stationary or moving.

In one example, the computing device may be configured to detect andidentify the objects and characteristics of the objects based oninformation received from multiple sources such as the image-capturedevice, the LIDAR device, the RADAR device, etc. However, in anotherexample, the computing device may be configured to identify the objectsbased on information received from a subset of the multiple sources. Forexample, images captured by the image-capture device may be blurred dueto a malfunction of the image-capture device, and in another example,details of the road may be obscured in the images because of fog. Inthese examples, the computing device may be configured to identify theobjects based on information received from the LIDAR and/or RADAR unitsand may be configured to disregard the information received from theimage-capture device.

In another example, the autonomous vehicle may be travelling in aportion of the road where some electric noise or jamming signals maycause the LIDAR device and/or RADAR device to operate incorrectly. Inthis case, the computing device may be configured to identify theobjects based on information received from the image-capture device, andmay be configured to disregard the information received from the LIDARand/or RADAR units. In yet another example, the computing device may beconfigured to rank these sources of information based on a condition ofthe road (e.g., fog, electronic jamming, etc.). The ranking may beindicative of which device(s) to rely on or give more weight to inidentifying the objects. For instance, if fog is present in a portion ofthe road, then the LIDAR and RADAR devices may be ranked higher than theimage-based device, and information received from the LIDAR and/or RADARdevices may be given more weight than respective information receivedfrom the image-capture device.

In general, as noted above, predetermined map data analysis anddynamically-built (e.g., with live sensor data) map data analysis may beutilized to identify safe or otherwise suitable regions for pullover, inaddition to (or alternative to) other methods/factors not describedherein. As the computing device identifies the suitable regions, it maybuild a list of those regions and store them in memory.

It should be understood that the identification of regions anddetermining left versus right edges/boundaries may be based on a countrythat the autonomous vehicle is in, such as whether the country theautonomous vehicle is in is a country in which driving on the right sideof the road is the norm or whether driving on the left side of the roadis the norm. In either case, the computing device can be configuredaccordingly.

FIG. 4A illustrates an example scenario for identifying a region inwhich to pull over and stop an autonomous vehicle 400. As shown, theautonomous vehicle 400 (e.g., a computing device of the autonomousvehicle), which is travelling in the +y-direction, may encounter anemergency scenario and need to pull over on the side of the road. InFIG. 4A, the computing device of the autonomous vehicle 400 may identifythree regions 402-406 ahead of the autonomous vehicle 400 and determinewhether the regions 402-406 are safe or otherwise suitable for pullingover and stopping the autonomous vehicle 400, in accordance with themethods and embodiments described above. The road may comprise two mainlanes 408A-B, a bicycle lane 408C, and a curb lane 408D.

The computing device may divide each region into chunks, as illustratedby the chunks 410 of region 402, for example. The computing device mayfirst analyze region 402, although the computing device may beconfigured to analyze other regions prior to or after analyzing region402. Due to the presence of another vehicle 412 currently parked in thecurb lane 408D of region 402 alongside the curb 414 of the road andtaking up significant space of region 402, the computing device maydetermine that region 402 is not suitable for pullover for at least thereason of the other vehicle 412. The computing device may then analyzeregion 404. In the example shown, the computing device may identify ared curb 416 (e.g., a “no parking zone”) as the rightmost boundary ofthe region 404, and may thus determine that region 404 is not suitablefor pullover for at least the reason of the red curb 416. The computingdevice may also identify an object 418 in region 404 to the left of theautonomous vehicle 400 and in lane 408A. The object 418 may be a staticobject such as a traffic cone or a wrecked vehicle, or the object 418may be a dynamic object such as a moving vehicle or other moving object(e.g., travelling in the +y direction). Although the object 418 may notaffect the computing device's decision of whether the region 404 issuitable for pullover, in scenarios in which the object 418 is a dynamicobject, the computing device may monitor the behavior of the object 418to determine if the autonomous vehicle 400 may need to avoid the object418 while in the process of pulling over or while continuing to follow anormal trajectory on the road.

The computing device may then analyze region 406. Moreover, thecomputing device may analyze region 406 using the method/algorithmdescribed above. As such, the computing device may first identify therightmost lane (L_r), 408B. The computing device may then obtain, fromthe predetermined map data, a set of road and lane boundaries (andpossibly other boundaries), and identify the boundaries to the right ofL_r, including the rightmost boundary (B_l) (e.g., the edge of L_r) asthe left edge of the bicycle lane 408C. The computing device may alsoidentify the rightmost boundary which can be considered the edge of theroad (B_e) (e.g., the edge where the curb lane 408D meets the curb 414).After identifying B_l and B_e, the computing device may then determinethe lateral distance from the center of L_r to B_l (D_l), and thelateral distance from the center of L_r to B_e (D_e). In the exampleillustrated in FIG. 4A, D_l plus the width of the autonomous vehicle 400(w) plus a small-valued threshold distance (T_p) (e.g., close to zero ornegative in value) is less than D_e, the computing device may determinethat the given chunk is suitable for pulling over the autonomous vehicle400 in region 406. Since region 406 is suitable for pullover, thecomputing device may then enable the autonomous vehicle 400 to park atlocation 420 in region 406. It should be understood that the value ofT_p may vary based on one or more factors, and in other similarscenarios may be of such a value where region 406 may not be suitablefor pullover. It should be also understood that, although not describedherein, the computing device may also perform the method/algorithm onregions 402 and 404 as well.

FIG. 4B illustrates another example scenario for identifying a region inwhich to pull over and stop an autonomous vehicle 450. As shown, theautonomous vehicle 450 (e.g., a computing device of the autonomousvehicle), which is travelling in the +y-direction, may encounter anemergency scenario and need to pull over on the side of the road. InFIG. 4B, the computing device of the autonomous vehicle 450 may identifya region 452 ahead of the autonomous vehicle 450 and determine whetheror not the region 452 is safe or otherwise suitable for pulling over andstopping the autonomous vehicle 450, in accordance with the methods andembodiments described above. The road may comprise two lanes between twocurbs 454.

The computing device may analyze region 452 and determine that it issuitable for pulling over the autonomous vehicle 450, since D_l plus wplus T_p is less than De(D_l is the same as D_e because B_l and B_e arethe same boundary). In the example shown, Tp may be negative and mayhave a magnitude of at least w in order to allow for region 452 to besuitable for pullover. The computing device may then enable theautonomous vehicle 450 to pull over and park at location 456. As shown,the road is a two-way road, and the computing device may identify twoobjects within the vicinity of the autonomous vehicle 450: vehicle 458travelling behind the autonomous vehicle 450 in the +y direction andvehicle 460 travelling in the −y direction in a different lane than theautonomous vehicle 450. In this example scenario, the computing devicemay determine that the road is such that the autonomous vehicle 450 maybe able to park in region 452 at location 456 and still leave room forvehicle 458 to safely pass the parked autonomous vehicle 450 alongtrajectory 462 or a similar trajectory without hitting the autonomousvehicle 450 or taking up too much space of the lane that vehicle 460 isin. Other examples are possible as well.

Referring back to FIG. 3, at block 304, the method 300 includes, basedon a speed of the autonomous vehicle and based on the region,determining a braking profile for reducing the speed of the autonomousvehicle while travelling within the region. The braking profile may bedetermined so as to account for safely controlling the autonomousvehicle while bringing the autonomous vehicle's speed down to zero. Forexample, pulling over the autonomous vehicle may involve sharp lateralmovements, so the braking profile may include a specific lower rate orrates at which to decrease the autonomous vehicle's speed as it moveslaterally to avoid control and stability issues that may result from ahigh rate of deceleration while moving sharply in a given direction.Other examples are also possible. In general, the braking profile may bedetermined by the computing device so as to quickly navigate theautonomous vehicle off of the road or to a stopping location.

The braking profile may be a function of the autonomous vehicle'scurrent speed. The braking profile may also comprise a plurality ofphases, each having a respective duration. The phases may includerespective rates at which to reduce the speed of the autonomous vehiclewhile travelling within the region. During each phase of the brakingprofile, the computing device may reduce the autonomous vehicle's speedat the respective rate. In some embodiments, the braking profile mayinclude a piecewise linear function associated with the phases. Thepiecewise linear function may include different line segmentsrepresentative of the respective rates for each phase. In otherembodiments, the braking profile may include one or more of a linearfunction, a quadratic function, an exponential function, a splinefunction, and other functions (piecewise and/or non-piecewise). Thecomputing device can also be programmable such that the respectiveduration of each phase can be predetermined or calibrated prior to theautonomous vehicle performing the example method 300.

In some embodiments, the braking profile may comprise three (or more)phases including a pre-maneuver phase, a maneuver phase which followsthe pre-maneuver phase, and a post-maneuver phase which follows themaneuver phase. The pre-maneuver phase may include a low rate of speedreduction (e.g., light braking), and in some examples, the autonomousvehicle may begin to move laterally towards its pullover destinationduring the pre-maneuver phase. The majority of the lateral movement maybe performed during the maneuver phase, which may also include a lowrate of speed reduction. The maneuver phase may also not require muchlongitudinal acceleration while the autonomous vehicle is undergoing themajority of the lateral displacement needed to pull over the autonomousvehicle. The post-maneuver phase may include a high rate of speedreduction (e.g., hard braking). In scenarios where the region includes alocation that is adjacent to a main road (e.g., on a shoulder of ahighway), the majority or entirety of the post-maneuver phase may beperformed while the autonomous vehicle is travelling within thatlocation off the main road. It should be understood, however, that insome examples, the lateral displacement of the autonomous vehicle in themaneuver phase may not be greater than the lateral displacement of theautonomous vehicle in the pre-maneuver and post-maneuver phases. In suchexamples, the total lateral displacement may be very small or there maybe no lateral displacement at all. It should also be understood that inother embodiments not described herein, the braking profile may compriseless than three phases.

In some embodiments, the braking profile may be based on any objectsand/or obstacles located within the region and/or outside of the region.For example, the suitable region may be right before a bridge orbarrier, so the braking profile may be determined such that theautonomous vehicle can come to a stop before reaching the bridge orbarrier. As such, each phase and respective rate may be based on atleast a portion of unobstructed drivable space within the region. Inother embodiments, the braking profile may be based on detected vehicleswithin the environment of the autonomous vehicle. For example, such asshown in FIG. 4B, there may be another vehicle travelling behind theautonomous vehicle, and so the braking profile may be determined suchthat the autonomous vehicle will not brake hard during the pre-maneuverphase and surprise the other vehicle. In general, the autonomous vehiclemay adjust its speed based on the behavior of objects in the same oradjacent lanes on the road (e.g., ahead of or behind the autonomousvehicle), such as the example just described, and such as an adjustmentmade when a nearby vehicle or other dynamic object moves from itscurrent lane to the lane in which the autonomous vehicle is travelling.Other examples are also possible.

At block 306, the method 300 includes, based on the braking profile,determining a trajectory such that the autonomous vehicle will travelwithin the region while reducing the speed of the autonomous vehicle.The determined trajectory may further be based on information obtainedfrom the region and other identified regions, such as informationdescribed with respect to block 302 of the method 300. The determinedtrajectory may be determined such that the autonomous vehicle will avoidany objects/obstacles within the region (and outside of the region, insome examples) while reducing the speed of the autonomous vehicle inaccordance with the braking profile. Further, in some embodiments, thecomputing device may utilize a conjugate gradient method and/or othermethods to optimize the determined trajectory.

In some embodiments, the trajectory may be determined so as to satisfyone or more constraints. The constraints may include minimizing thetrajectory's deviation from a normal driving trajectory (e.g., if theautonomous vehicle was to continue driving instead of pulling over)while traveling the distance covered in the pre-maneuver phase of thebraking profile. The constraints may also include only driving theautonomous vehicle on the existing road lane(s) or within the region soas to ensure that at no point in time during the pullover the autonomousvehicle travels beyond the road's far edge or other boundary. Theconstraints may further include the autonomous vehicle performing aminimal amount of steering during the post-maneuver phase as it comes toa complete stop within the region. The constraints may still furtherinclude ensuring that the autonomous vehicle slows down to avoid anymoving vehicles in front of the autonomous vehicle while travellingalong the trajectory. Other constraints are also possible.

FIG. 5 illustrates an example scenario for determining a braking profileand a trajectory such that an autonomous vehicle 500 will travel withinthe region while reducing the speed of the autonomous vehicle 500 inaccordance with the braking profile. As shown, the autonomous vehicle500 (e.g., a computing device of the autonomous vehicle), which istravelling in the +y-direction, may identify a region ahead in which topull over and stop the autonomous vehicle 500. For the sake of examplethe autonomous vehicle 500 is shown to be travelling at a current speedof 29 m/s, although other speeds are possible.

The braking profile of the autonomous vehicle 500 may include apre-maneuver phase 502, a maneuver phase 504, and a post-maneuver phase506. During the pre-maneuver phase 502, the computing device may beconfigured to decelerate the autonomous vehicle 500 at a low rate of 1m/s² for a duration of two seconds, thus bringing the speed of theautonomous vehicle 500 down to 27 m/s by the start of the maneuver phase504. During the maneuver phase 504, the computing device may beconfigured to decelerate the autonomous vehicle 500 at a low rate of 1m/s² for a duration of three seconds, thus bringing the speed of theautonomous vehicle 500 down to 24 m/s by the start of the post-maneuverphase 506. In some embodiments, a low rate of deceleration may be neededduring the maneuver phase 504 since the steering and lateral movement ofthe autonomous vehicle 500 may be largest during this phase. During thepost-maneuver phase 506, the computing device may be configured todecelerate the autonomous vehicle 500 at a higher rate of 4.8 m/s² for aduration of five seconds, thus bringing the speed of the autonomousvehicle 500 down to 0 m/s and stopping the autonomous vehicle 500 on theshoulder of the road at location 508, as shown.

The computing device may determine a trajectory 510 for the autonomousvehicle 500 in accordance with the braking profile. However, in somecases, including cases in which no emergency is detected by computingdevice, the computing device may be configured to instruct theautonomous vehicle 500 to continue along its current trajectory 512(e.g., default trajectory) rather than follow the trajectory 510 forpulling over in the region. As noted above, the trajectory 510 mayinclude more steering and lateral movement of the autonomous vehicle 500during the maneuver phase 504. As shown, the trajectory 510 may bedetermined such that the autonomous vehicle 500 will avoid objects suchas object 514 and object 516. In addition, the braking profile may bedetermined to account for a presence of objects such as object 516 inthe autonomous vehicle's 500 pullover region. For instance, if theautonomous vehicle 500 needs to come to a quick stop in the region toavoid colliding with object 516, the braking profile may be determinedsuch that the post-maneuver phase 506 includes hard braking (e.g., 4.8m/s² for five seconds, as shown, or 8 m/s² for three seconds, for harderbraking, etc.).

The example scenario of FIG. 5 illustrates a braking profile that may bedetermined based on a high initial (e.g., current) speed of theautonomous vehicle 500. It should be understood that at low speeds,various aspects of the braking profile and the determined trajectory maychange. For example, at low speeds, the duration of the pre-maneuverphase 502 may be reduced and the rate of deceleration during thepost-maneuver phase 506 may be substantially reduced, which may causethe autonomous vehicle 500 to stop smoothly. Other examples are alsopossible.

Referring back to FIG. 3, at block 308, the method 300 includesdetermining, by the computing device (or other device configured tocontrol the autonomous vehicle), instructions for pulling over andstopping the autonomous vehicle in the region in accordance with thedetermined trajectory. Further, at block 310, the method 300 includesstoring the instructions in a memory accessible by the computing device.As such, the computing device may be configured to receive informationindicative of a request or requirement to access and execute theinstructions so as to pull over and stop the autonomous vehicle.

The computing device may be configured to receive, from sensors anddevices coupled to the autonomous vehicle (e.g., via one or moresignals), information associated with, for example, condition of systemsand subsystems of the autonomous vehicle, such as sensor systems andother systems and subsystems. For example, information indicating thatthe autonomous vehicle is about to run out of gas or information that animportant sensor of the autonomous vehicle has failed may be taken intoaccount by the computing device in determining whether to access andexecute the instructions to pull over and stop the autonomous vehicle.As another example, the computing device may encounter/detect a scenario(of the road ahead and/or of the autonomous vehicle's systems) that itdoes not know how to handle, and thus the computing device may thendetermine that the autonomous vehicle needs to be pulled over. Otherexamples are also possible. Further, the computing device may beconfigured to receive information from a remote operator indicative of arequest to pull over and stop the autonomous vehicle in accordance withthe region, braking profile, trajectory, and instructions stored in thememory.

Still further, the computing device may be configured to receive amanual command/instruction (e.g., via a button or switch located insidethe autonomous vehicle) for the computing device to access and executethe instructions for pulling over the autonomous vehicle. The manualinstruction may be received by a driver of the autonomous vehicle orother person(s), and upon execution of the In some embodiments, afterdetecting an emergency scenario, the computing device may be configuredto provide the driver of the autonomous vehicle a window of time duringwhich the driver can take over manual control of the autonomous vehicleto either pull over the autonomous vehicle or continue driving, based onwhether the driver deems it necessary to pull over the autonomousvehicle or not.

The computing device may be configured to receive information associatedwith the surrounding environment of the autonomous vehicle, such asdriving conditions and road conditions (e.g., rain, snow, etc.). Forexample, information indicating that the road is icy or wet ahead of thevehicle may be taken into account by the computing device in determininga region, braking profile, and/or trajectory to pull over and stop theautonomous vehicle, and determining whether to access and execute theinstructions to pull over and stop the autonomous vehicle. Otherexamples are also possible.

While the computing device may be configured to periodically determinethe region, braking profile, trajectory, and instructions for pullingover and stopping the autonomous vehicle, the computing device may alsobe configured to periodically or continuously determine defaultinstructions for maintaining a current trajectory of the autonomousvehicle (e.g., current trajectory of 512 of FIG. 5) and maintaining thespeed of the autonomous vehicle so as to not pull over and stop theautonomous vehicle. The default instructions may also be stored inmemory accessible by the computing device. In some embodiments, at agiven point in time, the computing device may be configured to executethe default instructions unless an emergency scenario is detected (e.g.,a failure of one or more systems of the autonomous vehicle and/or of thecomputing device itself) and/or a manual override command is received,causing the computing device to access and execute the instructions topull over and stop the autonomous vehicle and divert from the currenttrajectory.

In some embodiments, after executing instructions to pull over and stopthe autonomous vehicle, the computing device may be configured to switchback to the current trajectory if the computing device determines thatthe emergency scenario is no longer present, if the computing devicedetermined the emergency scenario in error, and/or if the drivermistakenly directed the computing device to execute the instructions,among other possibilities. In such embodiments, the computing device mayinstruct the autonomous vehicle to switch back to the current trajectoryautomatically, or the driver may be enabled to either manually switch tothe current trajectory or take control of the vehicle (e.g., speed andsteering control).

The decision made by the computing device (e.g., one or more modules ofthe computing device) to access and execute the instructions to pullover the autonomous vehicle can be determined at the same module of thecomputing device that is configured to determine the pullovertrajectory, or can be determined at a different module. For example, ifinformation associated with both the pullover trajectory and the currenttrajectory is accessible by a lower-level module (e.g., a controllerresponsible for actuating the throttle/brakes and steering), thelower-level module can be configured to make the decision of whether ornot to execute the instructions to pull over the autonomous vehicle. Inthis case, the lower-level module may be configured to utilizeheuristics relating to determining whether a high-level moduleresponsible for determining the current trajectory has failed and if thehigh-level module has not determined/stored a new current trajectory ina particular amount of time (e.g., the high-level module has stoppedcontinuously determining the current trajectory). The heuristics mayalso relate to determining how recently the pullover trajectory wasgenerated and determining if the pullover trajectory is no longersuitable/valid for use (e.g., based on changing conditions of the road,or other factors). Other heuristics are also possible.

Executing the instructions to either continue along the currenttrajectory or pull over and stop the autonomous vehicle may involveexecuting the instructions in accordance with one or more controlstrategies. The control system of the autonomous vehicle may comprisemultiple control strategies that may be predetermined or adaptive tochanges in a driving environment of the autonomous vehicle, the drivingenvironment including the predicted actions of objects substantially infront of the autonomous vehicle, behind the autonomous vehicle, and/orto the side of the autonomous vehicle. Generally, a control strategy maycomprise sets of instructions or rules associated with trafficinteraction in various driving contexts. The control strategy, forexample, may comprise rules that determine a speed of the autonomousvehicle, steering angle, and a lane that the autonomous vehicle maytravel on while taking into account safety and traffic rules andconcerns (e.g., other vehicles stopped at an intersection andwindows-of-opportunity in yield situation, lane tracking, speed control,distance from other vehicles on the road, passing other vehicles, andqueuing in stop-and-go traffic, and avoiding areas that may result inunsafe behavior such as oncoming-traffic lanes, etc.). For instance, thecomputing device may be configured to determine, based on the identifiedregion, the braking profile, the determined trajectory, and theinstructions of blocks 302-310, respectively, a control strategycomprising rules for actions that control speed, steering angle, andlane of the autonomous vehicle. Further, a given control strategy (ormultiple strategies) may comprise a program or computer instructionsthat characterize actuators controlling the autonomous vehicle (e.g.,throttle, steering gear, brake, accelerator, or transmission shifter).

In some examples, the instructions provided by the computing device toadjust the control of the autonomous vehicle (e.g., speed, steering,etc.) may be based on road geometry, such as if the road is straight,curving slightly, curving sharply, etc.

As noted above, the computing device may be configured to periodicallyperform the methods disclosed herein. During a given “cycle” of themethod, the given cycle comprising identifying the region of the road,determining the braking profile, determining the trajectory, determiningthe instructions, and storing the instructions, for example, thecomputing device may be configured to reuse at least a portion of thedata stored in memory from one or more previous cycles. For example, apullover trajectory determined during a given cycle may be based atleast in part on a pullover trajectory determined during a cycle priorto the given cycle. As another example, information associated with theroad ahead of the autonomous vehicle such as road boundary information(e.g., D_l and D_e) may not change from one cycle to the next, so thecomputing device may use the same road boundary information insequential cycles (e.g., if the values of D_l and D_e have not changedsince the previous cycle). Other examples of reusing data are alsopossible.

In some embodiments, the disclosed methods may be implemented ascomputer program instructions encoded on a computer-readable storagemedia in a machine-readable format, or on other non-transitory media orarticles of manufacture. FIG. 6 is a schematic illustrating a conceptualpartial view of an example computer program product 600 that includes acomputer program for executing a computer process on a computing device,arranged according to at least some embodiments presented herein. In oneembodiment, the example computer program product 600 is provided using asignal bearing medium 601. The signal bearing medium 601 may include oneor more program instructions 602 that, when executed by one or moreprocessors (e.g., processor 113 in the computing device 111) may providefunctionality or portions of the functionality described above withrespect to FIGS. 1-5. Thus, for example, referring to the embodimentsshown in FIG. 3, one or more features of blocks 302-310 may beundertaken by one or more instructions associated with the signalbearing medium 601. In addition, the program instructions 602 in FIG. 6describe example instructions as well.

In some examples, the signal bearing medium 601 may encompass acomputer-readable medium 603, such as, but not limited to, a hard diskdrive, a Compact Disc (CD), a Digital Video Disk (DVD), a digital tape,memory, etc. In some implementations, the signal bearing medium 601 mayencompass a computer recordable medium 604, such as, but not limited to,memory, read/write (R/W) CDs, R/W DVDs, etc. In some implementations,the signal bearing medium 601 may encompass a communications medium 605,such as, but not limited to, a digital and/or an analog communicationmedium (e.g., a fiber optic cable, a waveguide, a wired communicationslink, a wireless communication link, etc.). Thus, for example, thesignal bearing medium 601 may be conveyed by a wireless form of thecommunications medium 605 (e.g., a wireless communications mediumconforming to the IEEE 802.11 standard or other transmission protocol).

The one or more programming instructions 602 may be, for example,computer executable and/or logic implemented instructions. In someexamples, a computing device such as the computing device described withrespect to FIGS. 1-5 may be configured to provide various operations,functions, or actions in response to the programming instructions 602conveyed to the computing device by one or more of the computer readablemedium 603, the computer recordable medium 604, and/or thecommunications medium 605. It should be understood that arrangementsdescribed herein are for purposes of example only. As such, thoseskilled in the art will appreciate that other arrangements and otherelements (e.g. machines, interfaces, functions, orders, and groupings offunctions, etc.) can be used instead, and some elements may be omittedaltogether according to the desired results. Further, many of theelements that are described are functional entities that may beimplemented as discrete or distributed components or in conjunction withother components, in any suitable combination and location.

While various aspects and embodiments have been disclosed herein, otheraspects and embodiments will be apparent to those skilled in the art.The various aspects and embodiments disclosed herein are for purposes ofillustration and are not intended to be limiting, with the true scopebeing indicated by the following claims, along with the full scope ofequivalents to which such claims are entitled. It is also to beunderstood that the terminology used herein is for the purpose ofdescribing particular embodiments only, and is not intended to belimiting.

What is claimed is:
 1. A method performed by a computing systemconfigured to control an autonomous vehicle, the method comprising:receiving, by at least one processor of the computing system, and fromone or more sensors in communication with the computing system, sensordata indicative of lane boundaries of a region of a road of travel aheadof the autonomous vehicle and one or more road boundaries indicating anedge of the road in the region; based on the sensor data and furtherbased on a size of the autonomous vehicle, the at least one processor:(i) determining a distance between the autonomous vehicle and the laneboundaries of the road, (ii) determining a distance between theautonomous vehicle and the one or more road boundaries, (iii) comparingthe determined distances to a predetermined threshold distance and tothe size of the autonomous vehicle, and (iv) based on the determineddistances satisfying the predetermined threshold, selecting the regionto be a region in which to pull over and stop the autonomous vehicle;based on a speed of the autonomous vehicle and based on the region,determining, by the at least one processor, a braking profile forreducing the speed of the autonomous vehicle while travelling within theregion; based on the braking profile, determining, by the at least oneprocessor, a trajectory the autonomous vehicle navigates within theregion while reducing the speed of the autonomous vehicle in accordancewith the braking profile; the at least one processor continuouslydetermining up-to-date instructions to cause the autonomous vehicle topull over and stop during travel as an environment of the autonomousvehicle changes and storing the up-to-date instructions in a memory ofthe computing system accessible by the at least one processor; and basedon the stored up-to-date instructions, the computing system causing theautonomous vehicle to pull over and stop in the region while navigatingalong the trajectory and reducing the speed of the autonomous vehicle inaccordance with the braking profile.
 2. The method of claim 1, whereindetermining the braking profile comprises: determining a plurality ofphases including a first phase, a second phase following the firstphase, and a third phase following the second phase, determining, foreach phase of the plurality of phases, a respective rate at which toreduce the speed of the autonomous vehicle while travelling within theregion, and determining respective lateral displacements the autonomousvehicle navigates within the region during the plurality of phases,wherein the lateral displacement of the autonomous vehicle during thesecond phase is at least equal to the lateral displacement of theautonomous vehicle during the first phase and the lateral displacementof the autonomous vehicle during the third phase, and wherein therespective rate of the third phase is greater than the respective ratesof the first phase and the second phase.
 3. The method of claim 2,wherein at least one phase of the plurality of phases is based on atleast a portion of unobstructed drivable space within the region, andwherein the respective rates include respective rates at which to reducethe speed of the autonomous vehicle while travelling within the portionof unobstructed drivable space.
 4. The method of claim 1, furthercomprising: continuously determining, by the at least one processor, asthe environment of the autonomous vehicle changes, up-to-date defaultinstructions for maintaining a current trajectory of the autonomousvehicle and maintaining the speed of the autonomous vehicle so as to notpull over and stop the autonomous vehicle; and the at least oneprocessor storing the up-to-date default instructions in the memoryaccessible by the at least one processor.
 5. The method of claim 1,further comprising: receiving, at the at least one processor,information indicative of a failure of one or more systems of theautonomous vehicle; and in response to receiving the informationindicative of the failure of the one or more systems of the autonomousvehicle, and based on the stored up-to-date instructions, the at leastone processor causing the autonomous vehicle to pull over and stop inthe region while navigating along the trajectory and reducing the speedof the autonomous vehicle in accordance with the braking profile.
 6. Themethod of claim 1, wherein the stored up-to-date instructions includeinstructions to adjust a steering angle of the autonomous vehicle so asto navigate the determined trajectory.
 7. The method of claim 1, whereindetermining the distance between the autonomous vehicle and the laneboundaries of the road and determining the distance between theautonomous vehicle and the one or more road boundaries comprises:determining a location of (i) a rightmost lane in the region, (ii) arightmost boundary of the rightmost lane in the region, and (iii) arightmost edge of the road in the region, and based on the determinedlocations, determining (i) a lateral distance from a center of therightmost lane to the rightmost boundary of the rightmost lane and (ii)a lateral distance from the center of the rightmost lane to therightmost edge of the road, wherein comparing the determined distancesto the predetermined threshold distance and to the size of theautonomous vehicle comprises making a determination of whether thelateral distance from the center of the rightmost lane to the rightmostboundary of the rightmost lane plus a width of the autonomous vehicleplus the predetermined threshold distance is less than the lateraldistance from the center of the rightmost lane to the rightmost edge ofthe road, and wherein selecting the region to be the region in which topull over and stop the autonomous vehicle comprises selecting the regionto be the region in which to pull over and stop the autonomous vehiclein response to the determination being that the lateral distance fromthe center of the rightmost lane to the rightmost boundary of therightmost lane plus the width of the autonomous vehicle plus thepredetermined threshold distance is less than the lateral distance fromthe center of the rightmost lane to the rightmost edge of the road. 8.The method of claim 1, further comprising: before comparing thedetermined distances to the predetermined threshold distance and to thesize of the autonomous vehicle, the at least one processor: receivingsensor data that indicates a speed limit of the road; and adjusting thepredetermined threshold distance based on the sensor data that indicatesthe speed limit of the road, wherein comparing the determined distancesto the predetermined threshold distance and to the size of theautonomous vehicle comprises comparing the determined distances to theadjusted predetermined threshold distance and to the size of theautonomous vehicle.
 9. The method of claim 1, further comprising: beforecomparing the determined distances to the predetermined thresholddistance and to the size of the autonomous vehicle, the at least oneprocessor: based on predetermined map data of the road stored in thememory accessible by the at least one processor, determining a width ofthe road in the region; and adjusting the predetermined thresholddistance based on the width of the road in the region, wherein comparingthe determined distances to the predetermined threshold distance and tothe size of the autonomous vehicle comprises comparing the determineddistances to the adjusted predetermined threshold distance and to thesize of the autonomous vehicle.
 10. A non-transitory computer readablemedium having stored thereon instructions that, upon execution by acomputing device configured to control an autonomous vehicle, cause thecomputing device to perform functions comprising: while the autonomousvehicle is travelling on a road along a trajectory at a speed and whilean environment of the autonomous vehicle changes, continuously (i)determining up-to-date instructions to cause the autonomous vehicle topull over and stop during travel and (ii) storing the up-to-dateinstructions in a memory accessible by the computing device, whereindetermining and storing the up-to-date instructions comprises: selectinga suitable region of the road ahead of an autonomous vehicle to be anup-to-date region in which to pull over and stop the autonomous vehiclewhile avoiding obstacles in the up-to-date region, based on the speed ofthe autonomous vehicle and based on the up-to-date region, determining abraking profile corresponding to the up-to-date region, the brakingprofile being for reducing the speed of the autonomous vehicle whiletravelling within the up-to-date region, and based on the brakingprofile, (i) determining the up-to-date instructions for causing theautonomous vehicle to pull over and stop in the up-to-date region whilenavigating along an adjusted trajectory and reducing the speed of theautonomous vehicle in accordance with the braking profile correspondingto the up-to-date region, and (ii) storing the up-to-date instructionsin a memory accessible by the computing device; and based on the storedup-to-date instructions, causing the autonomous vehicle to pull over andstop in the up-to-date region while navigating along the adjustedtrajectory and reducing the speed of the autonomous vehicle inaccordance with the braking profile corresponding to the up-to-dateregion.
 11. The non-transitory computer readable medium of claim 10, thefunctions further comprising determining the predetermined thresholddistance based on one or more of: a type of the road boundaries, a widthof the road, and a speed limit of the road.
 12. The non-transitorycomputer readable medium of claim 10, wherein the sensor data furtherindicates one or more objects ahead of the autonomous vehicle on theroad, and wherein determining the braking profile comprises determiningthe first rate, the second rate, and the third rate based on the one ormore objects such that the autonomous vehicle will avoid the one or moreobjects within the region while reducing the speed of the autonomousvehicle in accordance with the braking profile.
 13. The non-transitorycomputer readable medium of claim 12, wherein determining the trajectorycomprises determining a trajectory the autonomous vehicle navigateswithin the region while avoiding the one or more objects within theregion and while reducing the speed of the autonomous vehicle inaccordance with the braking profile.
 14. The non-transitory computerreadable medium of claim 12, wherein the one or more objects include oneor more of a traffic cone, a road boundary barrier, and another vehicle.15. A system comprising: at least one processor; and a memory havingstored thereon instructions that, upon execution by the at least oneprocessor, cause the system to perform functions comprising: receiving,from one or more sensors coupled to the autonomous vehicle, sensor dataindicative of lane boundaries of a region of a road of travel ahead ofthe autonomous vehicle and one or more road boundaries indicating anedge of the road in the region; based on the sensor data and furtherbased on a size of the autonomous vehicle: (i) determining a distancebetween the autonomous vehicle and the lane boundaries of the road, (ii)determining a distance between the autonomous vehicle and the one ormore road boundaries, (iii) comparing the determined distances to apredetermined threshold distance and to the size of the autonomousvehicle, and (iv) based on the determined distances satisfying thepredetermined threshold, selecting the region to be a region in which topull over and stop the autonomous vehicle; based on a speed of theautonomous vehicle and based on the region, determining a brakingprofile for reducing the speed of the autonomous vehicle whiletravelling within the region; based on the braking profile, determininga trajectory the autonomous vehicle navigates within the region whilereducing the speed of the autonomous vehicle in accordance with thebraking profile; determining instructions for causing the autonomousvehicle to pull over and stop in the region while navigating along thetrajectory and reducing the speed of the autonomous vehicle inaccordance with the braking profile; continuously determining up-to-dateinstructions to cause the autonomous vehicle to pull over and stopduring travel as an environment of the autonomous vehicle changes andstoring the up-to-date instructions in the memory accessible andexecutable by the at least one processor; and based on the storedup-to-date instructions, causing the autonomous vehicle to pull over andstop in the region while navigating along the trajectory and reducingthe speed of the autonomous vehicle in accordance with the brakingprofile.
 16. The system of claim 15, the functions further comprising:referring to predetermined map data of the road stored in the memory,wherein the predetermined map data includes information indicating atleast a portion of the lane boundaries of the road and at least aportion of the one or more road boundaries, and wherein determining thedistances, comparing the determined distances, and selecting the regionin which to pull over and stop the autonomous vehicle are based on thepredetermined map data of the road.
 17. The system of claim 15, whereinthe predetermined map data further includes information indicating atype of lanes of the road, a type of the road boundaries, roadintersections ahead of the autonomous vehicle, and one or more obstaclesahead of the autonomous vehicle.
 18. The system of claim 15, thefunctions further comprising: as the environment of the autonomousvehicle changes, continuously determining up-to-date defaultinstructions for maintaining a current trajectory of the autonomousvehicle and maintaining the speed of the autonomous vehicle so as to notpull over and stop the autonomous vehicle; and storing the up-to-datedefault instructions in the memory accessible and executable by the atleast one processor.
 19. The system of claim 15, the functions furthercomprising: receiving information indicative of a failure of one or moresystems of the autonomous vehicle; and in response to receiving theinformation indicative of the failure of the one or more systems of theautonomous vehicle, and based on the stored up-to-date instructions, theat least one processor causing the autonomous vehicle to pull over andstop in the region while navigating along the trajectory and reducingthe speed of the autonomous vehicle in accordance with the brakingprofile.
 20. The system of claim 15, wherein the braking profilecomprises a plurality of phases including respective rates at which toreduce the speed of the autonomous vehicle while travelling within theregion, and wherein the braking profile includes a piecewise linearfunction associated with the plurality of phases and representative ofthe respective rates.
 21. A method performed by a computing systemconfigured to control an autonomous vehicle, the method comprising:while the autonomous vehicle is travelling on a road along a trajectoryat a speed and while an environment of the autonomous vehicle changes,at least one processor of the computing system continuously (i)determining up-to-date instructions to cause the autonomous vehicle topull over and stop during travel and (ii) storing the up-to-dateinstructions in a memory of the computing system accessible by the atleast one processor, wherein determining and storing the up-to-dateinstructions comprises: the at least one processor selecting a suitableregion of the road ahead of an autonomous vehicle to be an up-to-dateregion in which to pull over and stop the autonomous vehicle whileavoiding obstacles in the up-to-date region; based on the speed of theautonomous vehicle and based on the up-to-date region, the at least oneprocessor determining a braking profile corresponding to the up-to-dateregion, the braking profile being for reducing the speed of theautonomous vehicle while travelling within the up-to-date region; basedon the braking profile, the at least one processor (i) determining theup-to-date instructions for causing the autonomous vehicle to pull overand stop in the up-to-date region while navigating along an adjustedtrajectory and reducing the speed of the autonomous vehicle inaccordance with the braking profile corresponding to the up-to-dateregion, and (ii) storing the up-to-date instructions in a memory of thecomputing system accessible by the at least one processor; and based onthe stored up-to-date instructions, the computing system causing theautonomous vehicle to pull over and stop in the up-to-date region whilenavigating along the adjusted trajectory and reducing the speed of theautonomous vehicle in accordance with the braking profile correspondingto the up-to-date region.